Data from: Seasonality modulates the predictive skills of diatom based salinity transfer functions
Cite this dataset
Goldenberg-Vilar, Alejandra et al. (2018). Data from: Seasonality modulates the predictive skills of diatom based salinity transfer functions [Dataset]. Dryad. https://doi.org/10.5061/dryad.g76p8m0
The value of diatoms as bioindicators of contemporary and palaeolimnological studies through transfer function development has increased in the last decades. While they represent a tremendous advance in (palaeo) ecology, these models also leave behind important sources of uncertainties that are often ignored. In the present study we tackle two of the most important sources of uncertainty in the development of diatom salinity inference models: the effect of secondary variables associated to seasonality and the comparison of conventional cross-validation methods with a validation based on independent datasets. Samples (diatoms and environmental variables) were taken in spring, summer and autumn in the freshwater and brackish ditches of the province of North Holland in 1993 and sampled again different locations of the same province in 2008-2010 to validate the models. We found that the abundance of the dominant species significantly changed between the seasons, leading to inconsistent estimates of species optima and tolerances. A model covering intra-annual variability (all seasons combined) provides averages of species optima and tolerances, reduces the effect of secondary variables due to the seasonality effects, thus providing the strongest relationship between salinity and diatom species. In addition, the ¨all-season¨ model also reduces the edge effects usually found in all unimodal-based calibration methods. While based on cross-validation all four models seem to perform relatively well, a validation with an independent dataset emphasizes the importance of using models covering intra-annual variability to perform realistic reconstructions.